Alfonso de la Vega

Reduction mammaplasty: An advantageous option for breast conserving surgery in large-breasted patients

Oncoplastic breast conserving surgery is a good approach for large-breasted women with breast cancer, as it increases the rate of breast conserving surgery, improves cosmetic results and prevents both cosmetic sequelae and the symptoms associated with macromastia. We reviewed ten publications in which 276 patients had been treated with bilateral reduction mammaplasty. All showed the same conclusion: women with breast cancer and macromastia candidates for breast conserving surgery could obtain clear oncological and cosmetic advantages and an improvement in quality of life if they were treated using bilateral reduction onco-therapeutic mammaplasty.

Subcutaneous ossifying lipoma: case report and review of the literature

Background:  Lipomas can undergo a variety of changes, one of which is ossification. Ossifying lipoma (OL), independent of bone tissue, is rare. No case has been reported with this heading in the dermatopathological literature.

Postoperative spindle cell nodule of the breast: Pseudosarcomatous myofibroblastic proliferation following endo-surgery.

Despite the frequent use of fine‐needle aspiration, core biopsy and surgery, postoperative spindle cell nodule (PSCN) is a rare pathological complication that may be diagnostically treacherous. Presented herein is the case of a 52‐year‐old woman who developed a 7 mm mammary nodular lesion 66 days after removal of an area of columnar cell hyperplasia involving cellular and architectural atypia, performed with the Mammotome Breast Biopsy System. The lesion was highly cellular and composed of intersecting fascicles of plump spindle cells with blunt‐ended elongated nuclei and nucleoli easily visible. Interspersed mononuclear cells and hemosiderin‐laden macrophages were evident. PSCN is a reactive, benign myofibroblastic proliferation. Differential diagnosis includes benign and malignant spindle cell lesions of the breast. Recognition of this reactive lesion will avoid overdiagnosis of spindle cell malignant tumor. Attention to clinicopathological and histological features should result in accurate recognition of this lesion.

Giant hamartoma of the breast treated by the mammaplasty approach

A 29-year-old Arabic woman without risk factors for breast cancer had experienced a slow and progressive enlargement of her left breast without other associated symptoms for approximately 7 years. Physical examination showed a considerable breast asymmetry, the left breast being bigger and the palpation of which showed a diffuse sensation of increased breast consistency due to a mobile mass situated in the middle of the breast. Mammography showed a well-circumscribed mass 14 cm in diameter, situated in the centre of the breast and occupying it entirely. The diagnosis of breast hamartoma was carried out by a core-needle biopsy. Surgical treatment, excision of the tumour and reconstruction of the breast were carried out using a mammaplasty approach with a Wise pattern (T-inverted) and two pedicles: lateral superior to displace the nipple areola complex (NAC) and an inferior one to increase the breast projection. A well-encapsulated tumour, with a white–grey capsule rich in capillary vessel, smooth, ovoid-shaped, measuring 15 cm and weighing 535 g was easily dissected from the normal breast tissue and removed; its pathological examination confirmed the previous diagnosis of hamartoma. Figure 1 shows the preoperative and postoperative appearances of the patient, lateral and cranial mammography and the aspect of the surgical specimen. Simple enucleation of hamartoma and secondary expansion of the breast tissue are often sufficient to restore breast symmetry but, sometimes, when the hamartoma has caused considerable enlargement of the breast for a long period of time, surgical treatment using a mammaplasty technique offers clear advantages.1,2 This surgical approach facilitates the removal of the tumour, the remodelling of the breast and the NAC relocation. Mammaplasty techniques are very useful tools for the surgical treatment of breast tumours, benign or malignant.3 We consider that these ought to be incorporated into the surgical training of the ‘breast surgeon’ responsible for the surgical treatment of the breast tumours. 216 LETTERS TO THE EDITOR

Towards a DSL for Educational Data Mining

Nowadays, most companies and organizations rely on computer systems to run their work processes. Therefore, the analysis of how these systems are used can be an important source of information to improve these work processes. In the era of Big Data, this is perfectly feasible with current state-of-art data analysis tools. Nevertheless, these data analysis tools cannot be used by general users, as they require a deep and sound knowledge of the algorithms and techniques they implement. In other areas of computer science, domain-specific languages have been created to abstract users from low level details of complex technologies. Therefore, we believe the same solution could be applied for data analysis tools. This article explores this hypothesis by creating a Domain-Specific Language (DSL) for the educational domain.

A Model-Driven Ecosystem for the Definition of Data Mining Domain-Specific Languages

Data mining techniques are making their entrance in nowadays companies, allowing business users to take informed decisions based on their available data. However, these business experts usually lack the knowledge to perform the analysis of the data by themselves, which makes it necessary to rely on experts in the field of data mining. In an attempt to solve this problem, we previously studied the definition of domain-specific languages, which allowed to specify data mining processes without requiring experience in the applied techniques. The specification was made through high-level language primitives, which referred only to familiar concepts and terms from the original domain of the data. Therefore, technical details about the mining processes were hidden to the final user. Although these languages present themselves as a promising solution, their development can become a challenging task, incurring in costly endeavours. This work describes a development ecosystem devised for the generation of these languages, starting from a generic perspective that can be specialized into the details of each domain.

Surgery of Cosmetic Sequelae After Breast‐Conserving Therapy

Abstract:  Although a considerable number of patients have an unfair cosmetic result after breast‐conserving therapy, which correlates with poor psychosocial functioning, surprisingly, really only very few patients undergo surgical correction. The purpose of this article was to report our experience in the surgical treatment of such patients and analyze a special subgroup which required bilateral reduction mammaplasty because of associated symptomatic macromastia and their desire to reduce their breast size. From July 2000 to November 2008, some 23 patients consulted for unsatisfactory cosmetic outcome after breast‐conserving therapy, fourteen of them accepting the surgical treatment proposed and these were operated upon. We used the following techniques: reduction mammaplasty of the contralateral breast for symmetrization (9), bilateral reduction mammaplasty (4), one mammaplasty and augmentation of contra‐lateral breast and one myocutaneous dorsi flap for surgical correction of ipsilateral breast. One patient had serious complications, having partial necrosis of the areola, fat and breast skin necrosis, and needed reoperation for surgical removal of necrotic tissue. Some factors such as obesity and heavy smoking habits could explain this. We were able to evaluate cosmetic outcome in ten patients; late cosmetic outcome was good in seven patients, fair in two and poor in one. Despite the fact that most patients treated by breast‐conserving therapy are satisfied with the fact that they have retained their breasts and minimized an unfair cosmetic outcome, cosmetic evaluation should be introduced as a matter of routine.

A Run-Time Detector of Hardworking E-Learners with Underperformance

Due to the lack of a face-to-face interaction between teachers and students in virtual courses, the identification of at-risk learners among those who appear to show normal activity is a challenge. Particularly, we refer to those who are very active in the Learning Management System, but their performance is low in comparison with their peers. To fix this issue, we describe a method aimed to discover learners with an inconsistent performance with respect to their activity, by using an ensemble of classifiers. Its effectiveness will be shown by its application on data from virtual courses and its comparison with the results achieved by two well-known outlier detection techniques.

Mortadelo: A Model-Driven Framework for NoSQL Database Design

In big data contexts, the performance of relational databases can get overwhelmed, usually by numerous concurrent connections over large volumes of data. In these cases, the support of ACID transactions is dropped in favour of NoSQL data stores, which offer quick responses and high data availability. Although NoSQL systems solve this concrete performance problem, they also present some issues. For instance, the NoSQL spectrum covers a wide range of database paradigms, such as key-value, column-oriented or document stores. These paradigms differ too much from the relational model, provoking that it is not possible to make use of existent, well-known practices from relational database design. Moreover, the existence of that paradigm heterogeneity makes difficult the definition of general design practices for NoSQL data stores. We present Mortadelo, a framework devised for the automatic design of NoSQL databases. Mortadelo offers a model-driven transformation process, which starts from a technology-agnostic data model and provides an automatically generated design and implementation for the desired NoSQL data store. The main strength of our framework is its generality, i.e., Mortadelo can be extended to support any kind of NoSQL database. The validity of our approach has been checked through the implementation of a tool, which currently supports the generation of column family data stores and offers preliminary support of document-based ones.

FLANDM: a development framework of domain-specific languages for data mining democratisation

Abstract Companies have an increasing interest in employing data mining to take advantage of the vast amounts of data their systems store nowadays. This interest confronts two problems: (1) business experts usually lack the skills required to apply data mining techniques, and (2) the specialists who know how to use these techniques are a scarce and valuable asset. To help democratise data mining, we proposed, in a previous work, the development of domain-specific languages (DSLs) that hide the complexity of data mining techniques. The objective of these DSLs is to allow business experts to specify analysis processes by using high-level primitives and terminology from the application domain. These specifications would then be automatically transformed into a low-level, executable form. Although these DSLs might offer a promising solution to the aforementioned problems, their development from scratch requires a considerable effort and, consequently, they are costly. In order to make these languages affordable, we present FLANDM, an ecosystem devised for the rapid development of DSLs for data mining democratisation. FLANDM provides a base infrastructure that can be easily customised for the particularities of each domain, enabling controlled and systematic reuse of previously developed artefacts. By using FLANDM, new DSLs for data mining democratisation can be defined achieving a 50% of reduction in their development costs.